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This work combines experimental and modeling investigations to monitor the dynamics of creaming in oil-in-water emulsions. Turbidity and Raman spectroscopy methods were compared for the experimental monitoring of creaming of n-hexadecane droplets in water. A creaming model is developed based on the convection-diffusion equation. The model accounts for the full droplet size distribution. The experimental data were used to identify the diffusion coefficient of the oil droplets and the Richardson–Zaki coefficient correcting the effect of the local volume fraction. The droplets’ size and concentration are varied to evaluate their effect on the creaming rate. The developed model could accurately predict the kinetics of creaming in the different considered cases. It can be used for emulsion stability studies, for instance, in food/pharmaceutical industries and/or for gravity separator design and monitoring.

In this work, we report the development of a highly sensitive biosensor for sulfapyridine detection based on an integrated bio micro-electromechanical system (Bio-MEMS) containing four gold working electrodes (WEs), a platinum counter electrode (CE), and a reference electrode (RE). Firstly, the cleaned WEs were modified with 4-aminophenylacetic acid (CMA). Then, (5-[4-(amino)phenylsulfonamide]-5-oxopentanoic acid (SA2BSA) was immobilized onto the transducers surface by carbodiimide chemistry. The analyte was quantified by competitive detection with SA2BSA immobilized on the WE toward a mixture of Ab155 antibody (with fixed concentration) and sulfapyridine. In order to obtain a highly sensitive biosensor, Ab155 was immobilized onto magnetic latex nanoparticles surface to create a 3D architecture (Ab-MLNp). Using electrochemical impedance spectroscopy (EIS), we investigated the influence of the Ab-MLNp on the sensitivity of our approach. The optimized system was analyzed, as competitive assay, with different concentrations of sulfapyridine (40 µM, 4 µM, and 2 nM) and with phosphate buffer solution. From data fitting calculations and graphs, it was observed that the EIS showed more linearity when Ab-MLNp was used. This result indicates that the magnetic latex nanoparticles increased the sensitivity of the biosensor.